Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add melodic-software/claude-code-plugins/plugin install animationWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/melodic-software/claude-code-plugins/learn-style)<a href="https://agentmods.dev/skills/melodic-software/claude-code-plugins/learn-style"><img src="https://agentmods.dev/badge/skills/melodic-software/claude-code-plugins/learn-style/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/melodic-software/claude-code-plugins/learn-style"><img src="https://agentmods.dev/badge/skills/melodic-software/claude-code-plugins/learn-style.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00099 | $0.02010 |
| Opus 5.5 | $0.00040 | $0.00804 |
| Sonnet 5.5 | $0.00020 | $0.00402 |
| Haiku 4.5 | $0.00010 | $0.00201 |
Grade A, and why
learn-style scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learn style
Turn a rotoscoped clip into a style pack: the numbers that make the style what it is, and a check
that tells whether a new film shares them. The pack holds statistics only; never copy traced
geometry, source frames or the clip into it. Read
reference/statistics.md first: what each statistic measures, how a
band is set, which statistics separate the woodcut style from a filtered near miss and from other
styles, and what the check still cannot separate.
Run each script as python3 ${CLAUDE_PLUGIN_ROOT}/scripts/pydeps.py run --data-dir "${CLAUDE_PLUGIN_DATA}" -- <script> ... (/animation:setup checks the requirements). Input is a
rotoscope work directory (d/dNNN.json traces and src/dNNN.png drawings). The scripts are
${CLAUDE_PLUGIN_ROOT}/skills/learn-style/scripts/learn.py,
${CLAUDE_PLUGIN_ROOT}/skills/learn-style/scripts/controls.py and the shared
${CLAUDE_PLUGIN_ROOT}/scripts/inkstats.py.
Pack format
styles/<name>/ in the plugin, tool-neutral:
| File | Written by | Holds |
|---|---|---|
style.json |
learn.py, then you |
palette (ink, paper, tone ramp), knobs (the seven-knob schema), stats (p10/p50/p90), timing, ref (the source's values), bands, check, measured_only, heldout (how each band was set and how it tested), brush, credit, rotoscope_fits |
STYLE.md |
you | the style in words for a reader: credit, knob table with each value marked measured or judgment and pointing at its style.json key, how to author in it, the validation result |
learn.py measures everything but credit, brush and the movement, camera and
backgrounds knobs, which it keeps from an existing style.json; fill those knobs from viewing
the source and mark them "basis": "judgment". check is learn.py's CHECK, the statistics
shown to separate, less any content-class row the source defines in fewer than two parts;
measured_only lists the subject-sensitive statistics the check prints but never judges. The
numbers live in style.json only; STYLE.md and other docs point at its keys.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday Changed · +3 lines · -53 tokens per session c7e38e42e3b9
- 3d ago Changed c7f9ebb7f07a
- 13d ago Changed · +1 lines 258f78004f00
- 17d ago Changed · +7 lines b7933db000aa
- 18d ago First seen · 101 lines · 152 tokens per session scan A e9da78071647
learn-style is a skill published in the GitHub repository melodic-software/claude-code-plugins (25 stars, last pushed today), licensed MIT. It adds 99 tokens to every session and 2,010 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-28.
Other skills, from other repositories
parallel-orchestrator
Manage parallel Claude Code workstreams using git worktrees. Use when: splitting large tasks across multiple workers, coordinating parallel development, monitoring worker progress, integrating completed work, analyzing work item documents (code reviews, issue lists). Triggers: parallel, orchestrator, worktrees…
parallel-worker
Execute focused implementation tasks in a parallel workflow. Use when: working on assigned files in a worktree, making checkpoint commits, signaling dependencies or blockers, completing orchestrator-assigned tasks. Triggers: worker, checkpoint, worktree, assigned scope, commit prefix, parallel task.
optimize-local-search
For NP-hard optimization: TSP, scheduling, assignment problems. Uses greedy construction + local improvement (2-opt, hill climbing).
pass-function-as-arg
For generic algorithms: strategy pattern, callbacks, configurable behavior. Pass functions as parameters to customize algorithm behavior.
solve-constraint-puzzle
For constraint satisfaction: Sudoku, scheduling, N-queens, logic puzzles, SAT-like problems, assignment problems. Uses propagate-then-search pattern.
test-with-examples
For example-driven development: test cases as specifications, input/output pairs, documentation through examples.